Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data

Xia Li, Ruibin Bai

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. The results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. The overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. The computational complexity of SGPR models is O(mn2), and it does not require lengthy model fitting process as SVR and ANN. This makes GP models more practicable for real-world practice in large-scale transportation data analyses.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings
EditorsDaoqiang Zhang, Yang Gao, Hujun Yin, Bin Li, Yun Li, Ming Yang, Frank Klawonn, Antonio J. Tallón-Ballesteros
PublisherSpringer Verlag
Pages143-153
Number of pages11
ISBN (Print)9783319462561
DOIs
Publication statusPublished - 2016
Event17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016 - Yangzhou, China
Duration: 12 Oct 201614 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9937 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016
Country/TerritoryChina
CityYangzhou
Period12/10/1614/10/16

Keywords

  • Freight vehicle
  • Gaussian Processes
  • Machine learning
  • Sparse approximation
  • Trajectory data
  • Travel time prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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